Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
# data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x19f0d7ef048>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 50

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x19f0ca5d978>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    lr = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, lr

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "C:\\Users\\Adam\\Anaconda3\\lib\\runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py", line 3, in <module>\n    app.launch_new_instance()', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\traitlets\\config\\application.py", line 658, in launch_instance\n    app.start()', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tornado\\ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tornado\\stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-6cdc3da43fe3>", line 21, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "C:\\Users\\Adam\\repo\\Face_generation_GAN\\problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "C:\\Users\\Adam\\repo\\Face_generation_GAN\\problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "C:\\Users\\Adam\\repo\\Face_generation_GAN\\problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "C:\\Users\\Adam\\repo\\Face_generation_GAN\\problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "C:\\Users\\Adam\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :param alpha: Used to control the leaky relu
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 5*5*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 5, 5, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=1, padding='valid')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
    
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        
        out = tf.tanh(logits)
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [21]:
import numpy as np


def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)

    # Including real label smoothing to the loss calculation
    # http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/
    # https://github.com/soumith/ganhacks#6-use-soft-and-noisy-labels
    # Suggesting is for numbers between 0.7 and 1.2, after 1.
    smooth = np.random.uniform(0.1, 0.3)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth)))

    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))

    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [22]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [23]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [24]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, 
          data_shape, data_image_mode, print_every=100, show_every=100):
    """
    Train the GAN
    :param epoch_count: Number of ecpochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    class GAN:
        def __init__(self, image_width, image_height, image_channels, 
                     z_dim, learning_rate, alpha=0.2, beta1=0.5):
            # Trying to avoid "Nesting violated for default stack of %s objects"
            # tf.reset_default_graph()

            self.input_real, self.input_z, self.lr = model_inputs(image_width, image_height, image_channels, z_dim)

            self.d_loss, self.g_loss = model_loss(self.input_real, self.input_z,
                                                  image_channels)

            self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, learning_rate, beta1)

    # Create the network
    net = GAN(data_shape[1], data_shape[2], data_shape[3], z_dim, learning_rate, beta1=beta1)

    # Used during show generator output
    sample_z = np.random.uniform(-1, 1, size=(72, z_dim))
    
    # To be used when running session
    samples, losses = [], []
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                
                # Rescale batch_image data from [-0.5, 0.5] to [-1, 1] as we are using tanh activation
                batch_images *= 2

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(net.d_opt, feed_dict={net.input_real: batch_images, net.input_z: batch_z})
                _ = sess.run(net.g_opt, feed_dict={net.input_z: batch_z, net.input_real: batch_images})

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = net.d_loss.eval({net.input_z: batch_z, net.input_real: batch_images})
                    train_loss_g = net.g_loss.eval({net.input_z: batch_z})

                    print("Epoch {}/{}   ".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:6.3f} ".format(train_loss_d),
                          "Generator Loss: {:6.3f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess=sess,
                                          n_images=16,
                                          input_z=net.input_z,
                                          out_channel_dim=data_shape[3],
                                          image_mode=data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [25]:
batch_size = 32
z_dim = 128
learning_rate = 0.0004
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2    Discriminator Loss:  0.770  Generator Loss:  3.074
Epoch 1/2    Discriminator Loss:  1.033  Generator Loss:  1.782
Epoch 1/2    Discriminator Loss:  1.554  Generator Loss:  0.486
Epoch 1/2    Discriminator Loss:  1.221  Generator Loss:  0.746
Epoch 1/2    Discriminator Loss:  1.108  Generator Loss:  0.841
Epoch 1/2    Discriminator Loss:  1.254  Generator Loss:  0.753
Epoch 1/2    Discriminator Loss:  1.035  Generator Loss:  1.015
Epoch 1/2    Discriminator Loss:  1.490  Generator Loss:  0.472
Epoch 1/2    Discriminator Loss:  0.954  Generator Loss:  1.159
Epoch 1/2    Discriminator Loss:  1.829  Generator Loss:  0.339
Epoch 1/2    Discriminator Loss:  0.974  Generator Loss:  1.349
Epoch 1/2    Discriminator Loss:  0.811  Generator Loss:  1.396
Epoch 1/2    Discriminator Loss:  1.438  Generator Loss:  0.557
Epoch 1/2    Discriminator Loss:  1.589  Generator Loss:  0.446
Epoch 1/2    Discriminator Loss:  0.869  Generator Loss:  1.654
Epoch 1/2    Discriminator Loss:  1.323  Generator Loss:  0.624
Epoch 1/2    Discriminator Loss:  0.887  Generator Loss:  1.240
Epoch 1/2    Discriminator Loss:  0.770  Generator Loss:  1.657
Epoch 2/2    Discriminator Loss:  0.933  Generator Loss:  1.311
Epoch 2/2    Discriminator Loss:  0.953  Generator Loss:  1.601
Epoch 2/2    Discriminator Loss:  0.969  Generator Loss:  1.128
Epoch 2/2    Discriminator Loss:  0.776  Generator Loss:  1.324
Epoch 2/2    Discriminator Loss:  0.746  Generator Loss:  1.483
Epoch 2/2    Discriminator Loss:  1.531  Generator Loss:  0.622
Epoch 2/2    Discriminator Loss:  1.080  Generator Loss:  0.800
Epoch 2/2    Discriminator Loss:  0.805  Generator Loss:  2.779
Epoch 2/2    Discriminator Loss:  1.117  Generator Loss:  0.868
Epoch 2/2    Discriminator Loss:  0.984  Generator Loss:  0.983
Epoch 2/2    Discriminator Loss:  1.054  Generator Loss:  2.425
Epoch 2/2    Discriminator Loss:  0.775  Generator Loss:  1.377
Epoch 2/2    Discriminator Loss:  0.823  Generator Loss:  1.261
Epoch 2/2    Discriminator Loss:  0.807  Generator Loss:  2.950
Epoch 2/2    Discriminator Loss:  0.750  Generator Loss:  1.303
Epoch 2/2    Discriminator Loss:  0.869  Generator Loss:  1.167
Epoch 2/2    Discriminator Loss:  1.015  Generator Loss:  0.983
Epoch 2/2    Discriminator Loss:  0.621  Generator Loss:  1.866
Epoch 2/2    Discriminator Loss:  0.879  Generator Loss:  1.252

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [28]:
batch_size = 32
z_dim = 128
learning_rate = 0.0008
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1    Discriminator Loss:  1.601  Generator Loss:  3.346
Epoch 1/1    Discriminator Loss:  1.475  Generator Loss:  0.806
Epoch 1/1    Discriminator Loss:  1.301  Generator Loss:  1.976
Epoch 1/1    Discriminator Loss:  1.546  Generator Loss:  0.536
Epoch 1/1    Discriminator Loss:  1.245  Generator Loss:  0.739
Epoch 1/1    Discriminator Loss:  1.040  Generator Loss:  0.942
Epoch 1/1    Discriminator Loss:  1.327  Generator Loss:  0.769
Epoch 1/1    Discriminator Loss:  1.674  Generator Loss:  0.442
Epoch 1/1    Discriminator Loss:  0.984  Generator Loss:  1.620
Epoch 1/1    Discriminator Loss:  1.491  Generator Loss:  0.733
Epoch 1/1    Discriminator Loss:  1.006  Generator Loss:  1.140
Epoch 1/1    Discriminator Loss:  0.868  Generator Loss:  2.041
Epoch 1/1    Discriminator Loss:  1.282  Generator Loss:  0.728
Epoch 1/1    Discriminator Loss:  1.125  Generator Loss:  0.830
Epoch 1/1    Discriminator Loss:  1.299  Generator Loss:  0.837
Epoch 1/1    Discriminator Loss:  1.266  Generator Loss:  0.684
Epoch 1/1    Discriminator Loss:  1.353  Generator Loss:  0.762
Epoch 1/1    Discriminator Loss:  1.224  Generator Loss:  1.067
Epoch 1/1    Discriminator Loss:  0.978  Generator Loss:  1.491
Epoch 1/1    Discriminator Loss:  1.162  Generator Loss:  0.994
Epoch 1/1    Discriminator Loss:  1.429  Generator Loss:  0.574
Epoch 1/1    Discriminator Loss:  1.269  Generator Loss:  0.877
Epoch 1/1    Discriminator Loss:  1.171  Generator Loss:  1.705
Epoch 1/1    Discriminator Loss:  1.057  Generator Loss:  1.124
Epoch 1/1    Discriminator Loss:  1.269  Generator Loss:  0.902
Epoch 1/1    Discriminator Loss:  1.191  Generator Loss:  0.940
Epoch 1/1    Discriminator Loss:  1.065  Generator Loss:  1.065
Epoch 1/1    Discriminator Loss:  1.166  Generator Loss:  0.838
Epoch 1/1    Discriminator Loss:  1.090  Generator Loss:  1.346
Epoch 1/1    Discriminator Loss:  1.273  Generator Loss:  0.666
Epoch 1/1    Discriminator Loss:  1.502  Generator Loss:  0.662
Epoch 1/1    Discriminator Loss:  1.286  Generator Loss:  0.784
Epoch 1/1    Discriminator Loss:  1.179  Generator Loss:  1.170
Epoch 1/1    Discriminator Loss:  1.126  Generator Loss:  1.172
Epoch 1/1    Discriminator Loss:  1.497  Generator Loss:  1.770
Epoch 1/1    Discriminator Loss:  1.173  Generator Loss:  0.883
Epoch 1/1    Discriminator Loss:  1.249  Generator Loss:  0.978
Epoch 1/1    Discriminator Loss:  2.339  Generator Loss:  3.154
Epoch 1/1    Discriminator Loss:  0.752  Generator Loss:  1.895
Epoch 1/1    Discriminator Loss:  1.181  Generator Loss:  1.071
Epoch 1/1    Discriminator Loss:  1.682  Generator Loss:  0.423
Epoch 1/1    Discriminator Loss:  1.120  Generator Loss:  1.501
Epoch 1/1    Discriminator Loss:  0.773  Generator Loss:  1.549
Epoch 1/1    Discriminator Loss:  1.192  Generator Loss:  0.874
Epoch 1/1    Discriminator Loss:  1.206  Generator Loss:  1.029
Epoch 1/1    Discriminator Loss:  1.312  Generator Loss:  0.860
Epoch 1/1    Discriminator Loss:  1.293  Generator Loss:  0.771
Epoch 1/1    Discriminator Loss:  1.221  Generator Loss:  0.878
Epoch 1/1    Discriminator Loss:  1.305  Generator Loss:  0.738
Epoch 1/1    Discriminator Loss:  1.236  Generator Loss:  0.815
Epoch 1/1    Discriminator Loss:  1.288  Generator Loss:  0.859
Epoch 1/1    Discriminator Loss:  1.289  Generator Loss:  1.158
Epoch 1/1    Discriminator Loss:  1.280  Generator Loss:  0.904
Epoch 1/1    Discriminator Loss:  1.181  Generator Loss:  0.932
Epoch 1/1    Discriminator Loss:  1.361  Generator Loss:  0.695
Epoch 1/1    Discriminator Loss:  1.339  Generator Loss:  0.673
Epoch 1/1    Discriminator Loss:  1.286  Generator Loss:  0.991
Epoch 1/1    Discriminator Loss:  1.392  Generator Loss:  0.587
Epoch 1/1    Discriminator Loss:  1.092  Generator Loss:  1.132
Epoch 1/1    Discriminator Loss:  1.129  Generator Loss:  0.818
Epoch 1/1    Discriminator Loss:  0.922  Generator Loss:  1.255
Epoch 1/1    Discriminator Loss:  1.073  Generator Loss:  1.632
Epoch 1/1    Discriminator Loss:  1.175  Generator Loss:  0.921

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.